Enterprise AI
We are technical implementers. We set up the platform your company has chosen, or help you choose it, build what it needs around your systems, and coach your teams until they use it well.
Updated
Because licenses arrive before the work changes. A platform switched on with default settings, joined to nothing, and handed to people with one training video gets tried for a week and left. What makes it stick is unglamorous: identity and data set up so people trust it, connections to the systems where the work lives, a few uses built for real jobs, and time with each team on its own work.
Hands on, with your people, from the first setting to the day it is part of the job.
Setup
ChatGPT Enterprise, Claude, Microsoft 365 Copilot or Gemini for Workspace: single sign-on, who sees what, retention, connectors to SharePoint, Drive and Slack, admin and audit. Or open weight models on your own hardware.
Implementation
Assistants over your own documents, agents for the repeat work, and connections to the systems your teams use every day, each tested before anyone relies on it.
Coaching
Sessions with each team on the tasks it actually does, not generic prompts. We train champions inside each team, write the playbooks, and hold office hours while habits form.
Governance
A use policy written in plain words, which data goes where, approvals where they matter, and a measure of who uses it and whether it is helping.
Four stages, one team throughout.
The one that fits the systems you already run and the data you hold. Most companies already live in Microsoft 365 or Google Workspace, and that shapes the answer. We set the options side by side against your own work before anything is bought, and where the data cannot leave the building, we run open weight models on your hardware.
Yes, and it is the most common place we start. Licenses on their own rarely change how work is done. We set the platform up properly, join it to your systems, and coach each team on its own work until it does.
With them, not around them. Identity, data boundaries, retention and audit are set up to your policies, and everything we configure is written down and handed over.
Everything: the setup and its notes, the assistants and agents we build, the playbooks for each team, and the people we trained to carry it on.
It depends on how many teams and systems, and how much of the work your own people do. We say it in writing after the first call.
Every project is built the way we work: we listen, build one useful thing first, run it, and stay.